In this paper, we propose instead a radiomics approach to automated image-based diagnosis of complex CVDs. Radiomics is the task of calculating a large number of imaging descriptors from delineated images, which has been widely used in cancer imaging [1]}. In the context of cine-MRI, radiomics describing changes in image appearence due to CVDs have not been exploited. The proposed method estimates a large number of radiomic features including statistical, shape, and textural descriptors and assess their ability to discriminate between different CVDs automtically and robustly within a machine learning framework based on SVMs. Training and cross-validation are carried out in this study based on a database of 100 cine-MRI cases corrresponding to five different subclasses, from the ACDC challenge of MICCAI 2017.
